Optaplanner：如何从JSON读取游戏装甲数据并根据重量＆＃x2B找到最佳装甲集。统计数据？

Question

elden ring 是一个热门游戏，背后有一些有趣的理论。

有数百件装甲，武器和咒语。根据玩家＆amp;项目统计是一个有趣的实用问题。

我一直想学习如何使用约束求解器，并且似乎存在一个很好的用例！

---

目标：

• 给出了游戏中所有装甲的清单，以
• 找到的装甲集（头部，胸部，腿部，手臂）：
  • 最高的镇定
  • 对于最低的重量

---

这是回购：

• https://github.com/gavinray97/elden-ring-optimizer-optaplanner

到目前为止

• 放置装甲数据
• github.com/gavinray97/elden-ring-optimizer-optaplanner/blob/master/src/src/main/resources/armor.json“ rel =“ nofollow noreferrer”>在此处 ：//github.com/gavinray97/elden-ring-optimizer-optaplanner/blob/master/src/src/main/java/java/java/java/domain/armorpiece.java“ rel =“ nofollow noreferrer”
• @PlanningEntity class for a combination of armor
• Tried to write Solver, doesn' t工作

更新

设法在下面的建议之后解决了该

问题

@PlanningSolution
public class ArmorSetComboPlanningSolution {

    public List<ArmorPiece> armorPieces;
    public Map<Integer, List<ArmorPiece>> armorByType;

    @ValueRangeProvider(id = "headRange")
    @ProblemFactCollectionProperty
    public List<ArmorPiece> headList;

    @ValueRangeProvider(id = "chestRange")
    @ProblemFactCollectionProperty
    public List<ArmorPiece> chestList;

    @ValueRangeProvider(id = "armsRange")
    @ProblemFactCollectionProperty
    public List<ArmorPiece> armsList;

    @ValueRangeProvider(id = "legsRange")
    @ProblemFactCollectionProperty
    public List<ArmorPiece> legsList;

    @PlanningEntityProperty
    public ArmorSet armorSet;

    @PlanningScore(bendableHardLevelsSize = 1, bendableSoftLevelsSize = 5)
    BendableLongScore score;

    ArmorSetComboPlanningSolution() {
    }

    ArmorSetComboPlanningSolution(List<ArmorPiece> armorPieces) {
        this.armorPieces = armorPieces;
        this.armorByType = armorPieces.stream().collect(groupingBy(ArmorPiece::armorCategoryID));
        this.headList = armorByType.get(0);
        this.chestList = armorByType.get(1);
        this.armsList = armorByType.get(2);
        this.legsList = armorByType.get(3);
        // Need to initialize a starting value
        this.armorSet = new ArmorSet(0L, this.headList.get(0), this.chestList.get(0), this.armsList.get(0), this.legsList.get(0));
    }
}

。

public class ArmorSetEasyOptimizer implements EasyScoreCalculator<ArmorSetComboPlanningSolution, BendableLongScore> {

    private final int TARGE_POISE = 61;
    private final double MAX_WEIGHT = 60.64;

    public ArmorSetEasyOptimizer() {
    }

    @Override
    public BendableLongScore calculateScore(ArmorSetComboPlanningSolution solution) {
        long hardScore = 0L;
        ArmorSet armorSet = solution.armorSet;

        if (armorSet.getTotalPoise() < TARGE_POISE) {
            hardScore--;
        }

        if (armorSet.getTotalWeight() > MAX_WEIGHT) {
            hardScore--;
        }

        long poiseRatio = (long) (armorSet.getTotalPoise() / (double) armorSet.getTotalWeight() * 100);

        long physicalDefenseScaled = (long) (armorSet.getTotalPhysicalDefense() * 100);
        long physicalDefenseToWeightRatio = (long) (physicalDefenseScaled / armorSet.getTotalWeight());

        long magicDefenseScaled = (long) (armorSet.getTotalMagicDefense() * 100);
        long magicDefenseToWeightRatio = (long) (magicDefenseScaled / armorSet.getTotalWeight());

        return BendableLongScore.of(
                new long[]{
                        hardScore
                },
                new long[]{
                        poiseRatio,
                        physicalDefenseScaled,
                        physicalDefenseToWeightRatio,
                        magicDefenseScaled,
                        magicDefenseToWeightRatio
                }
        );
    }
}

我

19:02:12.707 [main] INFO org.optaplanner.core.impl.localsearch.DefaultLocalSearchPhase - Local Search phase (1) ended: time spent (10000), best score ([0]hard/[179/3540/97/2750/75]soft), score calculation speed (987500/sec), step total (4046).

19:02:12.709 [main] INFO org.optaplanner.core.impl.solver.DefaultSolver - Solving ended: time spent (10000), best score ([0]hard/[179/3540/97/2750/75]soft), score calculation speed (985624/sec), phase total (2), environment mode (REPRODUCIBLE), move thread count (NONE).

[0]hard/[179/3540/97/2750/75]soft

ArmorSet (Weight: 36.3, Poise: 65, Physical: 35.4, Phys/Weight: 0.97, Magic: 27.5, Magic/Weight: 0.75 ) [
    head: Radahn Soldier Helm (Weight: 4.0, Poise: 5),
    chest: Veteran's Armor (Weight: 18.9, Poise: 37),
    arms: Godskin Noble Bracelets (Weight: 1.7, Poise: 1),
    legs: Veteran's Greaves (Weight: 11.7, Poise: 22)
]

Answer 1

这有点奇怪，因为您只需要一个计划实体实例。只有一个 Armorset 对象 - 求解器将在越来越接近最佳组合时分配不同的装甲件。

因此，您的简单得分计算器永远不需要进行任何循环。它只需采用单个 Armorset 的重量并保持平衡，并从中创建得分。

但是，即使我认为这种用例可能是通往约束求解器的学习路径，但某种蛮力算法也可以工作 - 您的数据集并不大。更重要的是，使用详尽的算法（例如蛮力），您最终可以确保达到最佳解决方案。

（也就是说，如果您想将这些装甲套件与特定特征匹配的问题增加问题，那么它可能足够复杂，以使蛮力变得不足

。为我。 :-)我更喜欢引导您更多的游戏。